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However, these methods suffer from two problems due to the diversity and uneven distribution of source sentences. First, their performance is highly dependent on the choice of a development set, which may lead to an unstable performance for testing. Second, the sentence level translation quality is not assured since tuning is performed on the document level rather than on sentence level. In contrast with the standard global training in which a single weight is learned, we propose novel local training methods to address these two problems. We perform training and testing in one step by locally learning the sentence-wise weight for each input sentence. Since the time of each tuning step is unnegligible and learning sentence-wise weights for the entire test set means many passes of tuning, it is a great challenge for the efficiency of local training. We propose an efficient two-phase method to put the local training into practice by employing the ultraconservative update. On NIST Chinese-to-English translation tasks with both medium and large scales of training data, our local training methods significantly outperform standard methods with the maximal improvements up to 2.0 BLEU points, meanwhile their efficiency is comparable to that of the standard methods.<\/jats:p>","DOI":"10.1145\/2637478","type":"journal-article","created":{"date-parts":[[2014,12,22]],"date-time":"2014-12-22T13:53:23Z","timestamp":1419256403000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Discriminative Training for Log-Linear Based SMT"],"prefix":"10.1145","volume":"13","author":[{"given":"Lemao","family":"Liu","sequence":"first","affiliation":[{"name":"Harbin Institute of Technology, China"}]},{"given":"Tiejun","family":"Zhao","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, China"}]},{"given":"Taro","family":"Watanabe","sequence":"additional","affiliation":[{"name":"National Institute of Information and Communications Technology, Japan"}]},{"given":"Hailong","family":"Cao","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, China"}]},{"given":"Conghui","family":"Zhu","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, China"}]}],"member":"320","published-online":{"date-parts":[[2014,12,19]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1327452.1327494"},{"volume-title":"Proceedings of the Annual Meeting of the Association for Computational Linguistics. 200--208","year":"2008","author":"Blunsom Phil","key":"e_1_2_1_2_1"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1992.4.6.888"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.116"},{"volume-title":"Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 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